README.md

---
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
base_model:
- deepseek-ai/DeepSeek-V3.1-Base
---

<div align="center">
<img src="./figures/NEX_logo.svg" width="20%"/>
</div>

---

<div align="center">
🏠 <a href="https://nex.sii.edu.cn"><b>Home&nbspPage</b></a>&nbsp&nbsp | &nbsp&nbsp
🤗 <a href="https://hf.co/collections/nex-agi/nex-n1"><b>Model</b></a>&nbsp&nbsp | &nbsp&nbsp
🤗 <a href="https://huggingface.co/datasets/nex-agi/agent-sft"><b>Data</b></a>&nbsp&nbsp | &nbsp&nbsp
📚 <a href="https://huggingface.co/papers/2512.04987"><b>Paper</b></a>&nbsp&nbsp | &nbsp&nbsp
💻 <a href="https://github.com/nex-agi/Nex-N1"><b>Code</b></a>
</div>

# Nex-N1.1

Nex is a next-generation, full-stack agentic platform that brings foundation models, synthetic data pipelines, RL training, agent frameworks, and deployment tools together in one unified ecosystem.
DeepSeek-V3.1-Nex-N1.1 is the latest flagship release of the Nex-N1 series — a post-trained model designed to highlight agent autonomy, tool use, and real-world productivity.
We are committed to making it easier than ever to build and deploy AI agents by offering researchers and entrepreneurs a high-performance, reliable, and cost-effective "out-of-the-box" agent system.

## What's New in N1.1

- **Enhanced Claude Skills Integration**: Significantly improved ability to work with Claude Skills, delivering a more seamless tool-calling experience
- **Improved Instruction Following**: Substantially enhanced instruction adherence with greater stability and accuracy in complex tasks
- **Upgraded Frontend Capabilities**: Specialized optimization for frontend development scenarios, excelling in HTML, CSS, and JavaScript generation
- **Advanced Vibe Coding**: Further enhanced general-purpose coding abilities to meet diverse development needs
- **200K Context Length**: Extended context window to 200K tokens, perfectly compatible with Claude Code and other long-context scenarios
- **MTP Support**: Added Multi-Token Prediction capability for improved inference efficiency

## Usage

### Local Deployment

We recommend `sglang` for serving Nex-series models locally:

```bash
python -m sglang.launch_server --model-path /path/to/your/model
```

### Function Calling

Nex-series models support robust function-calling capabilities. To maximize the function-calling capabilities of the Nex-series models, we modified the tool parser of `qwen3_coder`, see: <https://github.com/sgl-project/sglang/pull/13411>. To enable this feature, simply add the `--tool-call-parser qwen3_coder` flag when launching the server:

```bash
python -m sglang.launch_server --model-path /path/to/your/model --tool-call-parser qwen3_coder
```

### Mini Program Development

Nex-N1.1 is optimized for mini program development with enhanced 200K context support. For optimal performance, we recommend using Claude Code configured with both `context7` and a search MCP.

```shell
claude mcp add --transport http context7 https://mcp.context7.com/mcp --header "CONTEXT7_API_KEY: [CONTEXT7_API_KEY]"

claude mcp add --transport stdio serper-search --env SERPER_API_KEY=[SERPER_API_KEY]  -- npx -y serper-search-scrape-mcp-server
```

Refer to <https://github.com/upstash/context7> for more details on setting up `context7`.